Introducing Deep Learning Self-Adaptive Misuse Network Intrusion Detection Systems

Abstract

The intrusion detection systems (IDSs) are essential elements when it comes to the protection of an ICT infrastructure. A misuse IDS is a stable method that can achieve high attack detection rates (ADR) while keeping false alarm rates under acceptable levels. However, the misuse IDSs suffer from the lack of agility, as they are unqualified to adapt to new and 'unknown' environments. That is, such an IDS puts the security administrator into an intensive engineering task for keeping the IDS up-to-date every time it faces efficiency drops. Considering the extended size of modern networks and the complexity of big network traffic data, the problem exceeds the substantial limits of human managing capabilities. In this regard, we propose a novel methodology which combines the benefits of self-taught learning and MAPE-K frameworks to deliver a scalable, self-adaptive, and autonomous misuse IDS. Our methodology enables the misuse IDS to sustain high ADR, even if it is imposed on consecutive and drastic environmental changes. Through the utilization of deep-learning based methods, the IDS is able to grasp an attack's nature based on the generalized feature reconstructions stemming directly from the unknown environment and its unlabeled data. The experimental results reveal that our methodology can breathe new life into the IDS without the constant need for manually refreshing its training set. We evaluate our proposal under several classification metrics and demonstrate that the ADR of the IDS increases up to 73.37% in critical situations where a statically trained IDS is rendered totally ineffective.